大模型越强，解释越真实，但长和短有取舍。
Verbosity Tradeoffs and the Impact of Scale on the Faithfulness of LLM Self-Explanations
- 用新指标分析75个模型的解释真实性
- 模型越大，解释越忠实于真实原因
- 适合关注AI可解释性的研究者阅读
当被要求解释决策时，大语言模型常给出对人类而言看似合理的说明。但这些解释是否真实反映了决策的真实依据？本文分析了来自13个模型家族的75个模型在反事实真实性上的表现。研究揭示了简洁性与全面性之间的权衡，以及相关性真实性度量如何评估这一权衡，并考察了度量被操纵的可能性。基于此，我们提出两个新指标：phi-CCT，是原相关性反事实测试（CCT）的简化版，无需词元概率即可解释大部分方差；F-AUROC则消除了对干预分布不平衡的敏感性，能捕捉模型生成不同详细程度解释的能力。结果表明，更大的、更强大的模型在所有度量上均表现出更高的真实性。代码已公开于 https://github.com/google-deepmind/corr_faith。
原文摘要 · Abstract (English)
When asked to explain their decisions, LLMs can often give explanations which sound plausible to humans. But are these explanations faithful, i.e. do they convey the factors actually responsible for the decision? In this work, we analyse counterfactual faithfulness across 75 models from 13 families. We analyze the tradeoff between conciseness and comprehensiveness, how correlational faithfulness metrics assess this tradeoff, and the extent to which metrics can be gamed. This analysis motivates two new metrics: the phi-CCT, a simplified variant of the Correlational Counterfactual Test (CCT) which avoids the need for token probabilities while explaining most of the variance of the original test; and F-AUROC, which eliminates sensitivity to imbalanced intervention distributions and captures a model's ability to produce explanations with different levels of detail. Our findings reveal a clear scaling trend: larger and more capable models are consistently more faithful on all metrics we consider. Our code is available at https://github.com/google-deepmind/corr_faith.
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